Analysis of the peculiarities of forecasting financial and economic indicators for management decision-making

Abstract

The article analyses approaches to forecasting economic indicators in various areas of the economy, including macroeconomic, microeconomic indicators, financial and economic indicators of an enterprise's economic activity, and indicators of the effectiveness of implementing investment business projects. The main tasks of financial and economic forecasting are identified to improve the accuracy of forecasts of financial and economic indicators for the purpose of making informed management decisions. The influence of the stochasticity of the external economic environment on the indicators of enterprise performance, competitiveness, and changes in its market position is investigated. An analysis of the main mathematical forecasting methods in modern economics has been carried out. The basic methods of forecasting dynamic economic processes have been identified and analysed: the application of regression analysis methods based on statistics and probability models; forecasting the dynamics of economic processes using modern methods of artificial intelligence, machine learning and Big Data; forecasting the dynamics of economic processes using differential equations (or difference equations) with stochastic parameters. A systematic approach has been developed to create a mathematical model for solving the problem of forecasting the effectiveness of business projects, taking into account the influence of external economic environment factors. It is proposed to include the following factors: the influence of supply and demand on the price of goods, political and industry risks, volumes of goods supply and sales. An analysis of the peculiarities of forecasting financial and economic indicators of enterprises of various forms of management has been carried out. It has been proven that one of the promising areas of forecasting indicators for determining the future value of a business is the application of mathematical models based on the combined use of differential equations with stochastic elements and correlation and regression analysis.